A new approach to estimating the evoked hemodynamic response applied to dual channel functional near infrared spectroscopy.

BACKGROUND: Brain activity can be measured non-invasively by means of functional near-infrared spectroscopy (fNIRS) which records hemodynamics of the brain tissue. Sensitivity of fNIRS to the brain activity is however being affected by natural physiological brain hemodynamics (systemic interferences). Functional hemodynamic signal extraction from physiological interferences still remains a challenging task.

NEW METHOD: This paper presents a novel effective algorithm for real-time physiological interference reduction and recovery of the evoked brain activity using a dual channel fNIRS system.

RESULTS: Performance of the proposed algorithm was evaluated using both synthetic and semi-real fNIRS data using three different metrics of: 1) correlation coefficient (CC), relative mean square error (rMSE), percentage estimation error of peak amplitude (EPA ).

COMPARISON WITH EXISTING METHODS: The results were compared to those of ensemble empirical mode decomposition based recursive least squares (EEMD-RLS) method which has proved to have a better performance than other widely used algorithms such as block averaging, band-pass filtering and principal and/or independent component analysis. This study showed that the proposed method outperforms the EEMD-RLS method producing a smaller average rMSE and EPA and a larger average CC even in the cases of shorter signal lengths and smaller signal to noise ratio conditions.

CONCLUSIONS: The proposed method has no assumption on the amplitude, shape and duration of the hemodynamic response such as those needed in other previously reported methods. Moreover, it is computationally low cost and simple, and needs no parameter updating.